[Objective] This paper presents a collaborative filtering algorithm based on gray correlation analysis and time factor, aiming to address the low similarity resolvability and user’s interest drifting issues of the traditional algorithms. [Methods] First, we proposed a new method to calculate user similarity based on gray relational degree. Then, we used the time weight function to improve the Pearson correlation coefficients. Third, we created a hybrid similarity calculation method and made recommendation based on the neighbors of the target user. Finally, we used the MovieLens dataset to examine the new algorithm. [Results] Compared with the traditional collaborative filtering algorithms and those considering gray correlation analysis or time factor alone, the proposed algorithm reduced the mean absolute error (MAE). [Limitations] It takes the proposed algorithm longer time to calculate the hybrid similarity. [Conclusions] The hybrid similarity method improves the accuracy of recommended items for the target users and has a very good commercial promotion prospect.
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Wang Daoping,Jiang Zhongyang,Zhang Boqing. Collaborative Filtering Algorithm Based on Gray Correlation Analysis and Time Factor. Data Analysis and Knowledge Discovery, 2018, 2(6): 102-109.
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